WO2007066589A1 - Procédé et appareil pour examiner et diagnostiquer une maladie liée au mode de vie utilisant une spectroscopie de proche infrarouge - Google Patents

Procédé et appareil pour examiner et diagnostiquer une maladie liée au mode de vie utilisant une spectroscopie de proche infrarouge Download PDF

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Publication number
WO2007066589A1
WO2007066589A1 PCT/JP2006/324059 JP2006324059W WO2007066589A1 WO 2007066589 A1 WO2007066589 A1 WO 2007066589A1 JP 2006324059 W JP2006324059 W JP 2006324059W WO 2007066589 A1 WO2007066589 A1 WO 2007066589A1
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WIPO (PCT)
Prior art keywords
analysis
wavelength
inspection
wavelengths
light
Prior art date
Application number
PCT/JP2006/324059
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English (en)
Japanese (ja)
Inventor
Hirohiko Kuratsune
Akikazu Sakudo
Kazuyoshi Ikuta
Yasuyoshi Watanabe
Yoshiki Nishizawa
Yukiyoshi Hirase
Seiki Tajima
Yoshikazu Suganuma
Original Assignee
Fatigue Science Laboratory Inc.
Osaka University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Fatigue Science Laboratory Inc., Osaka University filed Critical Fatigue Science Laboratory Inc.
Priority to JP2007549101A priority Critical patent/JPWO2007066589A1/ja
Priority to EP06823538A priority patent/EP1959249A1/fr
Priority to US12/096,178 priority patent/US20090318814A1/en
Publication of WO2007066589A1 publication Critical patent/WO2007066589A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light

Definitions

  • 001 relates to equipment used for inspection and disconnection of external spectroscopy.
  • indexing as a diagnostic target, there is a case indexing (advancement of individual health based on the discovery of the stage of the disease. Detection of the stage of the disease and careful management, work measures according to the health condition, health The purpose is to protect the individual's health, and the other purpose is to hold the group as a group. The results of the health diagnosis are evaluated collectively, and healthy children are analyzed. Take appropriate health management measures or labor measures.
  • Urine disease, stroke, heart disease, hypertension, obesity, illness, uremia, etc. are collectively referred to as life, and if one of these conditions is observed, it is considered as life. Early detection and treatment are important because if one of these symptoms occurs, other symptoms often continue.
  • the above-mentioned portion is quantitatively analyzed.
  • a sample is injected into a cell, and an external spectroscope (eg,
  • the outside line is subject to a very small number of substances, Therefore, it is possible to obtain physical information without damaging the sample.
  • a method is disclosed to analyze milk or cells by analyzing the data obtained by using water molecules in the visible light and / or near infrared region to measure the milk in cows. It has been done. As another technique, it is disclosed in the form of patients with living bed symptoms in the Asia-Pacific region.
  • the subject is to provide a simple and frequent method for inspecting and cutting life by using external spectroscopy.
  • the method and device according to Ming irradiate the surrounding material of wavelength 4 to 5 or the animal material in the surrounding and detect its emitted light, transmitted light or transmitted light to obtain spectral data. It is characterized by quantitatively or qualitatively conducting a life-related test () by analyzing the degree of measurement or the specified length in the analysis data.
  • an inspection / constitution regarding life is performed.
  • the inspection / determination of life refers to urine disease, injury, liver injury, hypertension, illness, obesity, heart disease and stroke, or the degree of onset, or the onset of onset. If, it means that there is a small amount of information about height.
  • blood fluid including
  • other body, tissue, tissue exudate, or body part such as limbs.
  • the spectral data of the wavelengths above 2 are selected from 817 921 95g 987 10 0 04 008 1018 028, and the number of Great Walls of 5 of each length within 1040.
  • 2 is recommended for analysis and analysis using spectral data of the wavelengths above, and also for urinary non-urinary disease (, inflammatory diseases, gA, and other kidneys).
  • the analysis data related to Ming is used in the above or in the above.
  • the program of daily life related to Ming is to perform the inspection / disconnection using the analysis data created, updated or created.
  • Ming it is possible to objectively and easily carry out inspections and decisions regarding life. Because of its speed, it is especially useful when a large number of animals need to be tested simultaneously.
  • Ming for example: ⁇ It is possible to cut off the life by using traditional materials such as cleansing. As a result, it is also possible to inspect other parts of the body, such as the body and limbs, in the sample, without injuring the body.
  • Fig. 6 is a graph showing Coomans Po obtained by S C analysis of the material diluted in the examination of a person outside urinary illness related to Ming.
  • Inspecting with this equipment Adopting the method of (b) illuminating (a) the 4 to 25 O enclosure or the animal material in that enclosure, and (b) detecting its emitted, transmitted or transmitted emitted light. After obtaining the spect data by (c), the degree of the measurement or the specific length in the spect data is analyzed using the analysis data that was created, and the inspection and setting related to life are performed. There is an objective and easy way to make a decision about life in Japan. It is in the range of 4 to 25 or a part thereof (for example, 6 to O 2) of the wavelength for irradiating the material. After creating an analysis data of this length, it can be set as multiple walls containing the wavelengths required for inspection and measurement by this data.
  • a gen lamp or the like can be used, but it is not particularly limited.
  • the light emitted from the source is applied to the material directly or through a step such as a pipe. Therefore, it is possible to use a pre-spectroscopy method in which a spectroscope disperses the light before irradiating the sample, or a method that disperses the light in the irradiation.
  • a pre-spectroscopy method in which a spectroscope disperses the light before irradiating the sample, or a method that disperses the light in the irradiation.
  • the light from the source is decomposed in a predetermined manner to irradiate the sample with a series of wavelengths that are continuously changed. Then, the sample is irradiated with light that is decomposed at 2 in the range of 6 to O 2 and continuously changed at a wavelength of 2.
  • the reflected light, the transmitted light, or the transmitted light of the light applied to the material is detected by detection, and raw absorption data is obtained.
  • Raw raw spectral data Although it may be inspected and determined by an analysis data using the analysis data, data such as decomposing the pic in the obtained spectrum into element pics by a spectroscopic method or a method is used. It is preferable to use a computer to perform inspection and determination by analysis data. Examples of optical methods include binary and commutative methods, and examples of modal methods include commutative and network methods, but are not particularly limited.
  • the spectral data acquired at the time of measurement is used as the analysis data 2 and the analysis data obtained based on the analysis data is used. You can also inspect and disconnect. For example, if a large number of people are to be inspected at the same time, the inspection part should be the analysis data. At this time, an analysis data will be created at the time of measurement.
  • This method can create an analysis without teacher data. It can be applied to both
  • 002 data can be created by analysis. For example, when predicting urinary illness, or liver damage as a test related to life, the data sequence containing the long spectrum obtained by the spectrum analysis is decomposed into suadins by a singular solution, and the Or, extract the principal component that summarizes (severe) (component analysis). This makes it possible to use the collinear (higher explanation) component for analysis. Then, apply the explanatory variable as the parameter and the objective variable as the parameter. This makes it possible to create an analysis data that estimates the degree of the measurement length or the spectrum of a specific length.
  • PC P ncpa Componen Reg esson has been established as (Pa a eas qa es) (Sakiyuki, Akira Uda, Toshio Akai, Metox for Persons, Kodansha, 2002).
  • Other examples include CS (Cassca eas qa es) and the quadrature method.
  • CS Cassca eas qa es
  • Quantitative analysis composition was used, but for qualitative analysis composition, class component analysis was performed.
  • PC P ncpa Componen Ana ss) SC (so ndependen mode ngO Cass anaog) (k ea es eghbo s) etc.
  • SC method a principal component analysis is performed for each of a plurality of groups), and a class component component data is created. Then, the unknown charge is compared with the component de of each class and assigned to the class of the main component de with the best match.
  • spect vectors into classes based on classes such as SC method and knowledge.
  • the clear program (sot) is used to create and update the analysis data, or to use the created analysis data to perform the inspection and disconnection on the life from the sample data of the sump. It is a thing.
  • the Ming program should be provided as a computer-readable recording medium that records this. You can As the recording medium, magnetic storage such as a disk, a disk, a magnetic tape COCCWO
  • optical bodies such as W
  • electric storage bodies such as O
  • magnetic bodies such as O, but are not limited to these.
  • the wavelengths required for inspection and disconnection by the device are determined.
  • the composition can be further refined by irradiating the sample with a plurality of walls determined by the above method.
  • 817 921 95g 987 1004 008 008 1018 028 040 was determined to be an effective wavelength for inspection in the examination and determination of urinary diseases, so it was selected from Great Wall in the range of 5 of these lengths.
  • 835 908 912 917 963 993 1002 008 1034 and 1040 060 were determined to be effective wavelengths for urine disease inspection and determination, so they were selected from the Great Walls in the range of 5 of these lengths.
  • 2 Diabetes mellitus by analysis using long (up to 2-5 or 5-) spectrum data.
  • 914 915 915 919 967 994 008 012 1214 14 018 024 1030 034 was determined to be an effective wavelength for the test for the determination of urinary disease outside (, inflammatory disease, gA, and other kidneys).
  • urinary disease outside , inflammatory disease, gA, and other kidneys.
  • 667 679 917 955 975 1005 083 1085 was determined to be an effective wavelength for inspection in the inspections and routines related to everyday use, it was selected from Great Walls in the range of 5 of these lengths. ⁇ 5 pieces or 5 ⁇ ) It is preferable to perform routine inspections by analysis using long spectral data. These In the analysis, the analysis data is based on the selected number of long-spectra data, or by adding the spectral data of other wavelengths. 003 shows a summary of the analysis steps described above and the steps of life by de. In the destep, for example, it is used for a patient sample in a normal life, and external spectroscopy is performed, and then the spectrum data obtained is processed.
  • the analysis data is created by referring to the existing disconnection results. It is possible to evaluate the analysis data by using the analysis data created in this way to inspect / disconnect the sample, or to analyze the life inclusive of the data () used to generate the analysis data. it can. If the evaluation data can be obtained from the previously created analysis data, update the data and rebuild the data.
  • An analysis data can be created by this method, and it is possible to inspect / judg about life by analyzing the spectrum of the food by the same method and analyzing the obtained data by data.
  • the device can be configured by including (), (), () and (data and v) 4 elements. Below, I will explain in simple terms.
  • the source has the ability to measure light from a light source such as a genlamp or the like (around 4 to 25 or its range) as a measurement target.
  • a light source such as a genlamp or the like
  • An example is a type, in which light is projected to () via a flexible light beam.
  • a near-infrared spectroscope can be manufactured at low cost, which is a cost.
  • the wavelength required for inspection / cutting by the data is determined.
  • the composition can be further improved by irradiating the sample with a plurality of walls determined by the above method.
  • the measurement system has an external spectrometer.
  • External spectrographs generally illuminate the measurement with light and detect the emitted or transmitted light from these objects. Furthermore, the degree of incident light is determined for each detected light.
  • the formula 003 includes front spectroscopy and back light.
  • the spectrum is split before it is projected onto the measurement. , Detects light from the measured object and disperses it. It is also possible to use the equations for the position, front spectroscopy, and backshift.
  • reflected light emission There are three emission methods: reflected light emission, transmitted light emission, and emitted light emission.
  • the incident light and the incident light and transmitted light from the measured object are detected. Although the incident light and the human incident light enter, they are reflected by the object, and the light emitted to the outside of the object is detected again. It is also possible to use the following formulas: deviation of reflected light, transmission of transmitted light, and emission of reflected light.
  • the optical device can be configured by a semiconductor device such as a CC (Cage Coordinated Device), but it is not limited to this, and another receiver may be used. .
  • the optical device can also be configured with multiple stages.
  • the memory for storing data such as data, spectrum data, program, analysis data, etc., and arithmetic processing based on these data and programs can be configured by the arithmetic processing unit. It can be realized with a C chip or the like. Therefore, it is easy to make the device into a belt shape. The data described above is also written in the C chip or the like.
  • the analysis result in the data Physically, it displays the severity of life-related symptoms analyzed by the analysis data. For example, based on the class of qualitative data, urinary diseases, urine diseases, urine diseases, urine diseases, and normal persons are displayed. If the device is a strip type, it is preferable to use a rat spy for the results.
  • the inspection / judgment about life is not limited to just a specific judgment as described above, but also includes a judgment about life such as the quantitative value of a medical condition or seriousness, the determination of the onset risk, etc. is there.
  • a judgment about life such as the quantitative value of a medical condition or seriousness, the determination of the onset risk, etc. is there.
  • I will explain the fact that it is possible to control life by using near-infrared spectroscopy, but it is not limited by the following.
  • the spectrum of materials was measured by the following method. For normal people (No ma dono Se m) and urine disease, urinary disease, non-urinary disease, and hepatic people, Ps-diluted products were used as foods.
  • the obtained spectra were analyzed by the S C method, and the urinary disease model was constructed.
  • the known urinary diseases and the Ministry of Health and Welfare standards were followed.
  • class 4 was defined as urinary disease and class was defined as normal.
  • o ncuded ampes is the number of samples (spect number) used in the analysis, and number 3 of samples uses 3 data obtained by using 3 consecutive samples. It means that you have done it.
  • Pep ocessng indicates the reason, and Mean cene indicates that the point of the putt is moved to the heart of the dataset. There are Goba and Loca for cope, but I chose oca. Ma xm. . Is the maximum to be parsed "..” (indicates the number of components and has been selected up to 3. paa ... is the best for creating the de of the analysis ....
  • o 3 is the best
  • Cass4 is the best ac o
  • Pobab heshod is the threshold for judging that a class belongs
  • Ca b on ans is a mathematical adjustment that relaxes T a ns indicates the conversion, and None indicates that the conversion is not performed.
  • CS CS4 means Class 4 Class 4 respectively.
  • CS 3 means that ac o (component) is used in the class, and the number after 3 indicates the ac o number used.
  • Ac a means that the actual class is, and so on.
  • P ed means that the class predicted using the analytical data obtained by the S C analysis of the present is is, and so on.
  • Noma ch is a numerical value when it was not judged by both urine patient and healthy person. As shown in these results, it was found that the analysis data obtained by the S C analysis enables good discrimination of urine disease patients.
  • a aC means that the actual class is, and so on.
  • P C means that the class predicted using the analytical data obtained by S C analysis is, and so on.
  • Noma ch is a numerical value when it is judged neither urine disease nor healthy person.
  • Exc dedSampes is the result of predicting unknowns. As shown in 4, it was shown that urinary illness can be well controlled.
  • the analysis data thus assembled is stored as a file, and this file is called for the unknown charge, and the analysis data is used to predict which class the unknown charge will be classified into. This makes it possible to diagnose urinary diseases easily and quickly.
  • O nc ded ampes is the number of samples (spect number) used for the analysis, and the number of samples 3 3 is the data of 3 samples obtained by 3 consecutive samples.
  • Pep ocessng indicates the reason, and Mean cene indicates that the point of the putt is moved to the heart of the dataset.
  • Pobab heshod is Ca ba on ans indicates a mathematical adjustment that relaxes, and T ans O m indicates a transformation, and None indicates a transformation is not performed. Show.
  • [0065] 4 is C analyzed by the SC analysis of the present embodiment. . m nsP. Indicates.
  • 6 is the result of the class (ne cassDances)
  • 7 is the result of the classification (MscassnnCa on). 6 1 11 0 C 0
  • CS CS5 means Class Ni Class 5, respectively. Also, CS 3 means that ac o (component) is used in the class.
  • A means that the actual class is, and so on.
  • r means that the class predicted using the analysis data obtained by this S C analysis is, and so on.
  • No ma ch is a numerical value when it is judged as neither a urine patient nor a healthy person. As shown in these results, it was found that the analysis data obtained by the sW A analysis could be used to discriminate between them.
  • Ac aC means that the actual class is and so on.
  • P edC means that the class predicted using the analytical data obtained by SC analysis is, and so on.
  • Noma ch is a numerical value when it is judged neither urine disease nor healthy person.
  • Exc dedSampes is the result of predicting an unknown sump. As shown in Fig. 8, it was shown that good disconnection was possible.
  • 00705 indicates () in (), which was subjected to the above SC analysis. It is shown that the higher the value of (Dsc mng ngPowe) is, the longer the distance between the two classes is. That is, it is considered that the length of a particular peak is an effective wavelength between the healthy person and the urine patient.
  • the analysis data assembled by 007 is stored as a file, and this file is called for the unknown charge, and the class into which the unknown charge is classified is predicted by the analysis data. This makes it possible to diagnose urinary diseases easily and quickly.
  • the obtained spectra were used for the S C method, and the data for those who did not have urinary disease were constructed.
  • class 6 was defined as a person without urinary disease and class as a normal person.
  • sample number 36 means that 3 data obtained by 3 consecutive 3 samples were used.
  • Pep ocessng shows the reason, and Mean cene shows that the point of the putt is moved to the heart of the dataset.
  • Ma Ym m. . Is the maximum to be analyzed "" indicates the number of (components), and is selected up to 3.
  • p ... is the best for creating the analysis data. .
  • Pobab heshod indicates a threshold for judging that a person belongs to a certain class.
  • Ca ba o ans indicates a mathematical adjustment that alleviates.
  • T s indicates the conversion, and None indicates that the conversion is not performed.
  • [0076] 6 is C analyzed by the S C analysis of the present embodiment. . m nsP. Indicates.
  • the results of the class are shown in the following, and the results of the classification (Mscass Ca on) are shown in the following.
  • CS CS6 means Class Ni Class 6, respectively. Also, CS (3) 5 means that ac o (ingredient) is used in class 5.
  • Ac a means that the actual class is, and so on.
  • P ed means that the class predicted using the analysis data obtained by the S C analysis of this implementation is, and so on.
  • Noma ch is a numerical representation of the cases in which both non-urinary illness and healthy subjects were judged. As shown in these results, it was found that the analysis data obtained by the S C analysis could be used to discriminate between them.
  • Ac aC means that the actual class is, and so on.
  • P edC means that the class predicted using the analytical data obtained by S C analysis is, and so on.
  • Noma ch is urinary
  • the analysis data assembled as a result is stored as a file, and this file is called for unknown charges, and the analysis data predicts which class the unknown charges will be classified into. This makes it possible to easily and quickly disconnect people outside the urinary illness.
  • the obtained spectra were analyzed by the S C method and the usual data were generated.
  • the known G Gram Oxtransase
  • GP Guardin Transmutase
  • GP Gutan Transpease
  • sample number 288 means that 3 data obtained by 3 consecutive 96 samples were used.
  • Pep ocessng shows the reason, and Mean cene shows that the point of the putt is moved to the heart of the dataset.
  • Ma xmum. . Indicates the maximum number of "! (components) to be analyzed and selected up to 3.
  • p a F ". . Is the best way to create a parsed data.
  • Ca ba on ans indicates a mathematical adjustment that relaxes
  • T ans O m indicates a transformation
  • mooh indicates smoothing.
  • 008888 is C analyzed by the SC analysis of the present implementation. . mn. Indicates. In addition, 4 is the result of the class (ne cassDances) and 5 is the result of the classification (Mscass nCa on). In CS094, CS and CS2 mean Class 2 and Class 2, respectively. In addition, CS 35 means that ac o (ingredient) is used in the class.
  • Ac a means that the actual class is, and so on.
  • P e means that the class predicted using the analysis data obtained by the SCA analysis of this implementation is, and so on.
  • Noma ch is a numerical value of the case where neither normal nor healthy person is judged. As shown in these results, it was found that normal subjects can be well distinguished by the analysis data obtained by S C analysis.
  • the analysis data assembled as a result is stored as a file, and this file is called for the unknown charge, and the analysis data predicts which class the unknown charge will be classified into. This makes it possible to perform simple and fast routine operations.

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Abstract

L'invention concerne un procédé d'examen et de diagnostic d'une maladie liée au mode de vie, le procédé comprenant l'irradiation d’échantillons de test provenant d'un sujet et d'autres animaux avec une lumière ayant une longueur d'onde se trouvant dans la plage allant de 400 nm à 2500 nm ou une partie de cette plage, la détection de la lumière réfléchie, de la lumière émise ou de la lumière émise/réfléchie pour obtenir des données spectrales d'absorption et ensuite l'analyse des absorbances au niveau de toutes les longueurs d'onde mesurées ou d'une longueur d'onde spécifique avec l'aide d'un modèle analytique ayant été formé de manière préliminaire ; et un appareil pour celui-ci.
PCT/JP2006/324059 2005-12-06 2006-12-01 Procédé et appareil pour examiner et diagnostiquer une maladie liée au mode de vie utilisant une spectroscopie de proche infrarouge WO2007066589A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP2007549101A JPWO2007066589A1 (ja) 2005-12-06 2006-12-01 近赤外分光を用いた生活習慣病に関する検査・診断法および装置
EP06823538A EP1959249A1 (fr) 2005-12-06 2006-12-01 Procédé et appareil pour examiner et diagnostiquer une maladie liée au mode de vie utilisant une spectroscopie de proche infrarouge
US12/096,178 US20090318814A1 (en) 2005-12-06 2006-12-01 Method and apparatus for examination/diagnosis of lifestyle related disease using near-infrared spectroscopy

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JP2005352158 2005-12-06
JP2005-352158 2005-12-06

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WO2007066589A1 true WO2007066589A1 (fr) 2007-06-14

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008004665A1 (fr) * 2006-07-06 2008-01-10 Fatigue Science Laboratory Inc. Procédé et appareil pour tester un cancer, un lupus érythémateux systémique (sle) ou un syndrome d'anticorps antiphospholipide à l'aide de rayons proches de l'infrarouge
JP2019513071A (ja) * 2016-03-06 2019-05-23 ユニバーシテイト ヘント タンパク質に関する翻訳後修飾の直接的赤外線分析

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Publication number Priority date Publication date Assignee Title
EP2302359A1 (fr) * 2009-09-24 2011-03-30 Université De Reims Champagne-Ardenne Spectroscopie de sérum en infrarouge pour l'évaluation non invasive de fibrose hépatique chez des patients souffrant d'une maladie chronique du foie
FR3049090B1 (fr) * 2016-03-21 2021-06-25 Sebastien Jean Serge Dupont Dispositif d'authentification biometrique adaptatif par echographie, photographies en lumiere visible de contraste et infrarouge, sans divulgation, a travers un reseau informatique decentralise

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WO2001075420A1 (fr) 2000-03-31 2001-10-11 Japan As Represented By President Of Kobe University Procede et dispositif pour detecter des mastites au moyen de lumiere visible et/ou de lumiere infrarouge proche
JP2002005827A (ja) 2000-06-19 2002-01-09 Kanazawa Kazuki 被検体の情報を得る方法
WO2004023974A2 (fr) * 2002-09-10 2004-03-25 Euro-Celtique, S.A. Appareil et procede de mesure non invasive de fractions sanguines
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